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ECONOMETRICS AND BUSINESS STATISTICS

Applied Econometrics

Tutorial 2

1. Let yi, xi, wi be three i.i.d. random variables with mean zero and variance equal to Gy(2) , Gx(2) and Gw(2) , respectively. Suppose also that E(yi xi ) = µyx , E(yi wi ) = µyw and E(wi xi ) = µwx .

Compute cov[(yi − xi ), (wi − xi )].

2. Page 299, Ex. 6.8. parts a, b, c, d, e. (Carter, Griffiths, Lim - Principles of Econometrics, 5th Ed. (2017))

3. Practice on EViews and learn about some of the interpretation of indicator variables: Use the utown.wfl dataset to study the effect of living near a major University (called University Town) on house prices. The three variables in the data set that you will need are price (measured in $1000 units, utown (an indicator that takes the value of 1 if the house is located near the university) and SQFT (house size, measured in square feet).

a. Estimate the model PRICE = a1+a2 UT0wN + e and hence estimate the house price premium for living near the university.

b. Create an indicator variable DTOWN (downtown) for houses that are not near the university by defining DTOWN = 1 - UTOWN, and then estimate the model PRICE = F1+F2 DT0wN + e. Use this model to estimate the discount for housing that is not near the university.

c. Estimate the model PRICE = y1 DT0wN + y2 UT0wN + e (this model has no intercept). Use this model to estimate the mean prices for downtown houses and uptown houses.

d. Comment on why the R2 and SSE measures of fit for each of these models in a., b. and c. are the same.

e. Estimate the model PRICE = 61 + 62 DT0wN + 62 UT0wN + e and then comment on what happens.

f. Estimate the model PRICE = u1 + e . Interpret your estimated parameter û 1 . Then determine the proportions of UTOWN and DTOWN houses in the sample, and hence find a relationship between û 1, 1 and 2 .